Temporal electronic phenotyping by mining careflows of breast cancer patients.

In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent health...

Descripción completa

Detalles Bibliográficos
Publicado en:	Journal of Biomedical Informatics Vol. 66; pp. 136 - 148
Autores principales:	Dagliati, A., Sacchi, L., Bellazzi, R., Tibollo, V., Pavesi, L., Zambelli, A., Holmes, J.H.
Formato:	research Journal Article
Publicado:	Academic Press Inc. Feb2017
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.